An open source corpus and automatic tool for section identification in Spanish health records.

BACKGROUND: Electronic Clinical Narratives (ECNs) store valuable individual's health information. However, there are few available open-source data. Besides, ECNs can be structurally heterogeneous, ranging from documents with explicit section headings or titles to unstructured notes. This lack of structure complicates building automatic systems and their evaluation. OBJECTIVE: The aim of the present work is to provide the scientific community with a Spanish open-source dataset to build and evaluate automatic section identification systems. Together with this dataset, the purpose is to design and implement a suitable evaluation measure and a fine-tuned language model adapted to the task. MATERIALS AND METHODS: A corpus of unstructured clinical records, in this case progress notes written in Spanish, was annotated with seven major section types. Existing metrics for the presented task were thoroughly assessed and, based on the most suitable one, we defined a new B2 metric better tailored given the task. RESULTS: The annotated corpus, as well as the designed new evaluation script and a baseline model are freely available for the community. This model reaches an average B2 score of 71.3 on our open source dataset and an average B2 of 67.0 in data scarcity scenarios where the target corpus and its structure differs from the dataset used for training the LM. CONCLUSION: Although section identification in unstructured clinical narratives is challenging, this work shows that it is possible to build competitive automatic systems when both data and the right evaluation metrics are available. The annotated data, the implemented evaluation scripts, and the section identification Language Model are open-sourced hoping that this contribution will foster the building of more and better systems.

Developing a Mobile App for Monitoring Medical Record Changes Using Blockchain: Development and Usability Study.

BACKGROUND: Although we are living in an era of transparency, medical documents are often still difficult to access. Blockchain technology allows records to be both immutable and transparent. OBJECTIVE: Using blockchain technology, the aim of this study was to develop a medical document monitoring system that informs patients of changes to their medical documents. We then examined whether patients can effectively verify the monitoring of their primary care clinical medical records in a system based on blockchain technology. METHODS: We enrolled participants who visited two primary care clinics in Korea. Three substudies were performed: (1) a survey of the recognition of blockchain medical records changes and the digital literacy of participants; (2) an observational study on participants using the blockchain-based mobile alert app; and (3) a usability survey study. The participants' medical documents were profiled with HL7 Fast Healthcare Interoperability Resources, hashed, and transacted to the blockchain. The app checked the changes in the documents by querying the blockchain. RESULTS: A total of 70 participants were enrolled in this study. Considering their recognition of changes to their medical records, participants tended to not allow these changes. Participants also generally expressed a desire for a medical record monitoring system. Concerning digital literacy, most questions were answered with "good," indicating fair digital literacy. In the second survey, only 44 participants-those who logged into the app more than once and used the app for more than 28 days-were included in the analysis to determine whether they exhibited usage patterns. The app was accessed a mean of 5.1 (SD 2.6) times for 33.6 (SD 10.0) days. The mean System Usability Scale score was 63.21 (SD 25.06), which indicated satisfactory usability. CONCLUSIONS: Patients showed great interest in a blockchain-based system to monitor changes in their medical records. The blockchain system is useful for informing patients of changes in their records via the app without uploading the medical record itself to the network. This ensures the transparency of medical records as well as patient empowerment.

Comparison of MetaMap and cTAKES for entity extraction in clinical notes.

BACKGROUND: Clinical notes such as discharge summaries have a semi- or unstructured format. These documents contain information about diseases, treatments, drugs, etc. Extracting meaningful information from them becomes challenging due to their narrative format. In this context, we aimed to compare the automatic extraction capacity of medical entities using two tools: MetaMap and cTAKES. METHODS: We worked with i2b2 (Informatics for Integrating Biology to the Bedside) Obesity Challenge data. Two experiments were constructed. In the first one, only one UMLS concept related with the diseases annotated was extracted. In the second, some UMLS concepts were aggregated. RESULTS: Results were evaluated with manually annotated medical entities. With the aggregation process the result shows a better improvement. MetaMap had an average of 0.88 in recall, 0.89 in precision, and 0.88 in F-score. With cTAKES, the average of recall, precision and F-score were 0.91, 0.89, and 0.89, respectively. CONCLUSIONS: The aggregation of concepts (with similar and different semantic types) was shown to be a good strategy for improving the extraction of medical entities, and automatic aggregation could be considered in future works.

Entity Extraction for Clinical Notes, a Comparison Between MetaMap and Amazon Comprehend Medical.

Extracting meaningful information from clinical notes is challenging due to their semi- or unstructured format. Clinical notes such as discharge summaries contain information about diseases, their risk factors, and treatment approaches associated to them. As such, it is critical for healthcare quality as well as for clinical research to extract those information and make them accessible to other computerized applications that rely on coded data. In this context, the goal of this paper is to compare the automatic medical entity extraction capacity of two available entity extraction tools: MetaMap (MM) and Amazon Comprehend Medical (ACM). Recall, precision and F-score have been used to evaluate the performance of the tools. The results show that ACM achieves higher average recall, average precision, and average F-score in comparison with MM.

What makes clinical documents helpful and engaging? An empirical investigation of experience sharing in an online medical community.

BACKGROUND: Social media have emerged as a platform for experience and knowledge sharing in the medical community. The online medical community is garnering increasing research attention; however, there is a lack of understanding of what factors influence the helpfulness and engagement of experience sharing in the community. METHODS: Clinical documents manifest physicians' experience and knowledge. This study fills the knowledge gap by investigating what elements of clinical documents contribute to the helpfulness of sharing clinical documents online and what influence member engagement. Clinical documents follow certain architecture to specify their structure and semantics for exchange (e.g., HL7 C-CDA). Accordingly, the structural elements of clinical documents may influence document helpfulness for the online community. Member engagement is one of the indicators of community success. We collected 6514 clinical documents from a real-world online medical community, and normalized them with the structural elements of HL7 C-CDA. We performed regression analyses to identify the structural elements that have significant impacts on document helpfulness and member engagement. RESULTS: The results show that some structural elements of clinical documents such as assessment, chief complaints, medications, physical exams, procedures, results, and vital signs sections have positive effects whereas assessment and plan, general status, history and past illness of patients, instructions, problem and review of systems have negative effects on the helpfulness of clinical documents. The results also reveal that structural elements such as family history, history of past illness, medication, physical exam, review of systems, and vital signs positively; whereas assessment, assessment and plan, instruction, and result negatively; influence member engagement. CONCLUSIONS: The findings provide guide on how to improve the effectiveness of sharing clinical experience online. The new and in-depth insights may contribute to the success of online medical communities and the quality of medical decisions.

Semantic preservation of standardized healthcare documents in big data.

BACKGROUND: Standardized healthcare documents have a high adoption rate in today's hospital setup. This brings several challenges as processing the documents on a large scale takes a toll on the infrastructure. The complexity of these documents compounds the issue of handling them which is why applying big data techniques is necessary. The nature of big data techniques can trigger accuracy/semantic loss in health documents when they are partitioned for processing. This semantic loss is critical with respect to clinical use as well as insurance, or medical education. METHODS: In this paper we propose a novel technique to avoid any semantic loss that happens during the conventional partitioning of healthcare documents in big data through a constraint model based on the conformance of clinical document standard and user based use cases. We used clinical document architecture (CDAR) datasets on Hadoop Distributed File System (HDFS) through uniquely configured setup. We identified the affected documents with respect to semantic loss after partitioning and separated them into two sets: conflict free documents and conflicted documents. The resolution for conflicted documents was done based on different resolution strategies that were mapped according to CDAR specification. The first part of the technique is focused in identifying the type of conflict in the blocks that arises after partitioning. The second part focuses on the resolution mapping of the conflicts based on the constraints applied depending on the validation and user scenario. RESULTS: We used a publicly available dataset of CDAR documents, identified all conflicted documents and resolved all the them successfully to avoid any semantic loss. In our experiment we tested up to 87,000 CDAR documents and successfully identified the conflicts and resolved the semantic issues. CONCLUSION: We have presented a novel study that focuses on the semantics of big data which did not compromise the performance and resolved the semantic issues risen during the processing of clinical documents.

What Defines Good Quality in a Clinical Study Report?

Although clinical study reports (CSRs) are one of the central documents in clinical development, little attention has been paid to those features of such reports that determine their overall quality. While the ICH E3 guideline mentions a few quality attributes for CSRs, there are hardly any publications concerned with determining the key quality features of CSRs. This aspect is also often missing in medical writing textbooks. This study set out to identify the elements that contribute to the overall quality of clinical study reports using guided interactive introspection in a medical writing group (N = 28). All medical writers had a science background with a PhD; their professional experience ranged from 1 to 17 years (mean: 3.3 years). In total, 16 quality items were determined, which were grouped into 5 major areas: language, document structure, numerical information, audience focus, and company context. Ordered by decreasing importance, the 10 most important elements were: correctness, completeness, regulatory compliance, clarity of structure, conciseness, consistency, timeliness, appropriate language and style, adequate conclusion, and alignment with clinical project. The quality of clinical trial reports proved to be multifactorial and multidimensional; the overall quality cannot be represented by any single quality item. To achieve optimal quality, medical writers need to apply professional judgment to balance between those elements that were identified as contributing to overall document quality.